field llm: langchain.llms.base.BaseLLM [Required]#
LLM wrapper to use.
field revised_answer_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'question'], output_parser=None, partial_variables={}, template="{checked_assertions}\n\nQuestion: In light of the above assertions and checks, how would you answer the question '{question}'?\n\nAnswer:", template_format='f-string', validate_template=True)#
Prompt to use when questioning the documents.
pydantic model langchain.chains.LLMMathChain[source]#
Chain that interprets a prompt and executes python code to do math.
Example
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain(llm=OpenAI())
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field llm: langchain.llms.base.BaseLLM [Required]#
LLM wrapper to use.
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into Python code that can be executed in Python 3 REPL. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```python\n${{Code that solves the problem and prints the solution}}\n```\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n\n```python\nprint(37593 * 67)\n```\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: {question}\n', template_format='f-string', validate_template=True)#